Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment

Abstract

Fang and Gong (2017) develop a procedure to detect potential over-billing of Medicare by physicians. In their empirical analysis, they use aggregated claims data that can overstate the number of services performed due to features of Medicare billing. In this comment, I show how auditors can use detailed claims-level data to better target improper overbilling. (JEL H51, I13, I18, J22, J44)

Detecting fraud, waste, and abuse in substance use disorder treatment

“…13,14,16,21 Billing for an impossible number of hours is an additional potential fraud indicator used elsewhere, including in Medicare. 22,23 For numerical claims data fields that can be falsified, Benford's Law may be useful. Benford's Law states that in a series of data, the probability distribution of leading digits of numbers is not uniform.…”

“…However, from claims data, one cannot ascribe intent-claims may reflect data entry errors, be outright false (no services provided), be an exaggeration of the number or intensity of services provided, or may reflect provided, but unnecessary, services. [21][22][23] Moreover, statistical outliers in billing frequencies may reflect providers' real concerns about undertreating a patient. 22,26,27 Other potential indicators of unethical SUD treatment practices are not recorded in an insurance claim and thus would be missed in a claims data-based algorithm.…”

“…Common indicators of fraud include duplicate or near‐duplicate billing for procedures, unbundling of lab tests, a greater than expected number of patient encounters per provider, and patterns of treatment that do not meet the needs of a patient's diagnosis 13,14,16,21 . Billing for an impossible number of hours is an additional potential fraud indicator used elsewhere, including in Medicare 22,23 …”

“…Stakeholders listed several potential indicators of fraud that require knowledge of intent or clinical context and the ability to distinguish between falsification and exaggeration: billing for services not rendered, patients referred to services from which they cannot benefit, low follow‐up rates, and requiring patients to step through every possible level of care. However, from claims data, one cannot ascribe intent—claims may reflect data entry errors, be outright false (no services provided), be an exaggeration of the number or intensity of services provided, or may reflect provided, but unnecessary, services 21–23 . Moreover, statistical outliers in billing frequencies may reflect providers' real concerns about under treating a patient 22,26,27 …”